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The above results remind us that the impact of E-waste recycling on environmental quality of Jinghai should be evaluated soon. Moreover, it is urgent for the government to prohibit E-waste import and its processing by outdated ways. The future studies such as pollutant details of drinking water, air, and soil in the area as well as epidemiological investigations on the harmful effect to children must be performed eagerly. All the data available do provide a compelling case for immediate action in both countries to address workplace health and safety and waste management.

A knitted fabric strain sensor made from silver-plated conductive yarn and nylon spandex covered yarn is presented. Ten sensing zones are embedded into the fabricated running tights throughweft knitting technology. Physical activity tests are conducted to establish the linear relationship between the angle of the knee joint and the change in resistance of the sensing zones. A corresponding change in resistance relative knee joint movement is applied to identify three kinds of knee joint dynamics, namely, walking, sitting, and squatting, as well as four kinds of movement modes under the walking state of knee joint, including running, walking, climbing, and descending steps. The middle sensor region of the knee joint also has an obvious influence on the sensing performance of knee joint movement. Four modes of motion are extracted. Maximum resistance, average resistance, variance resistance, and median resistance are the main resistance characteristic values. Two kinds of motion cycles, namely, stand phase and swing phase, are the main time characteristic values. This thesis cites the decision tree algorithm and describes the method of realizing gait recognition.

Knitted fabric sensors have been widely used as strain sensors in the sports health field and its large strain performance and structure are suitable for human body movements. When a knitted structure is worn, different human body movements are reflected through the large strain deformation of fabric structure and consequently change the electrical signal. Here, the mechanical and electrical properties of highly elastic knitted sweatpants were tested under large strain. This sensor has good sensitivity and stability during movement. Compared with traditional motion monitoring, this technique divides the walking cycle into two stages, namely, stance and swing phases, which can be further subdivided into six stages. The corresponding resistance characteristic values can accurately distinguish the gait cycle. Analysis on hysteresis and repeatability revealed that the sensor exhibits a constant electrical performance. Four kinds of motion postures were predicted and judged by comparing the resistance characteristic range value, peak value calculation function and time axis. The measured sensor outputs were transferred to a computer via 4.0 Bluetooth. Matlab language was used to detect the status through a rule-based algorithm and the sensor outputs.

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